A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Signature analysis calibration of a school energy model using hourly data
An EnergyPlus model of a school with displacement ventilation and radiant slab thermal control was calibrated using signature analysis. Capacity to model these technologies is relatively recent in simulation software, and little has been published on calibration of energy models with these systems. The calibration of the as-built model required eleven adjustment iterations. Between iteration one and three; a group of weather-independent factors were modified. Between iterations four and eight; weather-dependent parameters were updated. The last three iterations addressed weather-independent electric demand parameters, for which hourly data were used. The calibrated model complied with the mean bias error (MBE) and coefficient of variation (CV) requirements of ASHRAE Standard 14. Hourly data were key to meeting the criteria for an adequate match between simulation estimates and the measured data. The MBE and CV of root-mean-squared error were 0.5% and 7% for electricity with daily data and 5% and 9% for gas with weekly data.
Signature analysis calibration of a school energy model using hourly data
An EnergyPlus model of a school with displacement ventilation and radiant slab thermal control was calibrated using signature analysis. Capacity to model these technologies is relatively recent in simulation software, and little has been published on calibration of energy models with these systems. The calibration of the as-built model required eleven adjustment iterations. Between iteration one and three; a group of weather-independent factors were modified. Between iterations four and eight; weather-dependent parameters were updated. The last three iterations addressed weather-independent electric demand parameters, for which hourly data were used. The calibrated model complied with the mean bias error (MBE) and coefficient of variation (CV) requirements of ASHRAE Standard 14. Hourly data were key to meeting the criteria for an adequate match between simulation estimates and the measured data. The MBE and CV of root-mean-squared error were 0.5% and 7% for electricity with daily data and 5% and 9% for gas with weekly data.
Signature analysis calibration of a school energy model using hourly data
Kandil, Alaa-Eldin (author) / Love, James A. (author)
Journal of Building Performance Simulation ; 7 ; 326-345
2014-09-03
20 pages
Article (Journal)
Electronic Resource
English
| British Library Conference Proceedings | 1997
Using Data Visualization Tools for the Calibration of Hourly DOE-2.1 Simulations
| British Library Conference Proceedings | 1995
Hourly Rainfall Simulation Using Daily Data
| Springer Verlag | 2022